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A fast recursive least squares adaptive second order Volterra filter and its performance analysis

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2 Author(s)
Junghsi Lee ; Dept. of Electr. Eng., Utah Univ., Salt Lake City, UT, USA ; Mathews, V.J.

A fast, recursive least squares (RLS) adaptive nonlinear filter modeled using a second-order Volterra series expansion is presented. The structure uses the ideas of fast RLS multichannel filters, and has a computational complexity of O(N3) multiplications, where N-1 represents the memory span in number of samples of the nonlinear system model. A theoretical performance analysis of its steady-state behaviour in both stationary and nonstationary environments is presented. The analysis shows that, when the input is zero mean and Gaussian distributed, and the adaptive filter is operating in a stationary environment, the steady-state excess mean-squared error due to the coefficient noise vector is independent of the statistics of the input signal. The results of several simulation experiments show that the filter performs well in a variety of situations. The steady-state behaviour predicted by the analysis is in very good agreement with the experimental results

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Signal Processing, IEEE Transactions on  (Volume:41 ,  Issue: 3 )